Middleware announced the launch of Ops AI, a new tool that autonomously detects and resolves application issues in production environments.
In early testing, the feature enabled engineering teams to improve productivity by nearly 80%.
Ops AI builds on core capabilities such as data querying, anomaly detection, and infrastructure scaling to reproduce issues and simplify troubleshooting. Engineers simply install the Middleware APM agent and connect their GitHub repository. From there, Ops AI detects issues, identifies the root cause, and generates a fix as a pull request.
What Ops AI can do for you:
- Error Monitoring and Summarization: It collects errors from the front-end, back-end, error logs, and code exceptions, presenting them in an easily readable format that displays the error type, error message, exception, and error code line, along with a complete stack trace. Companies can also track and manage errors more efficiently by assigning statuses like 'reviewed', 'resolved', and 'ignored'.
- Detailed Root Cause Analysis: Middleware's Ops AI identifies the exact location that caused the error by tracing a link to the codebase. It provides detailed error information, including the file name, code line, stack trace, and even related variables and version details. This makes it easy to understand what went wrong, allowing engineers to start fixing issues immediately without wasting time searching through logs or code.
- One-click error resolution: With Ops AI, engineering teams can look at the root cause and a recommended one-click fix on a single screen. If the Ops AI is 95% confident in a bug fix, it can also generate a pull request (PR) with the fixed bugs through this interface to save time and get the application up and running again.
- Continuous learning: Ops AI improves as it observes the platform and learns from historical data, including bug occurrences and fixes, enabling companies to reduce downtime of their production systems.
Middleware has been using OpsAI for its own system, resulting in an impressive uptick in AI-powered bug fixes. "We started using Ops AI at Middleware, and it now resolves over half of our production issues automatically. In tests with multiple customers, we've seen a detection-to-resolution rate of over 70%. We believe this is a game-changer for observability," said Laduram Vishnoi, Founder and CEO of Middleware.
The new Ops AI platform can increase on-call developer productivity by more than 80% and reduce mean time to respond (MTTR) by 5 times.
Middleware is also planning to expand Ops AI to cover Logs and Kubernetes monitoring. The goal is for Ops AI to detect issues in real-time within Kubernetes, before DevOps teams even start investigating. It will generate a ready-to-use root cause analysis (RCA), saving engineers significant time on debugging.
Vishnoi believes that the future of observability isn't just about seeing problems—it's about solving them instantly. Middleware is building that future with Ops AI. As the company continues to expand across the stack, its vision remains clear: eliminate toil, accelerate resolution, and empower engineering teams to focus on what truly matters—shipping great products.
The Latest
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...